Key to automatically generate natural scene images is to properly arrange among various spatial elements, especially in the depth direction. To this end, we introduce a novel depth structure preserving scene image generation network (DSP-GAN), which favors a hierarchical and heterogeneous architecture, for the purpose of depth structure preserving scene generation. The main trunk of the proposed infrastructure is built on a Hawkes point process that models the spatial dependency between different depth layers. Within each layer generative adversarial sub-networks are trained collaboratively to generate realistic scene components, conditioned on the layer information produced by the point process. We experiment our model on a sub-set of SUNdataset with annotated scene images and demonstrate that our models are capable of generating depth-realistic natural scene image.
翻译:自动生成自然场景图像的关键是适当安排各种空间要素,特别是深度方向。 为此,我们引入了一种新的深度结构,保存场景图像生成网络(DSP-GAN),这一结构有利于等级和多样性结构,以保持场景生成。拟议基础设施的主干柱建在一个霍克斯点过程上,该过程模拟不同深度层之间的空间依赖。在每一层内,对立亚网络进行协作培训,以生成现实的场景组件,以点过程产生的层信息为条件。我们用附加说明的场景图像在SUN数据集子集上试验我们的模型,并证明我们的模型能够生成深度现实的自然场景图像。